Alian Amirhosein, Avery James, Mylonas George
The Hamlyn Centre, Imperial College London, London, United Kingdom.
Front Robot AI. 2024 Aug 9;11:1372936. doi: 10.3389/frobt.2024.1372936. eCollection 2024.
The integration of soft robots in medical procedures has significantly improved diagnostic and therapeutic interventions, addressing safety concerns and enhancing surgeon dexterity. In conjunction with artificial intelligence, these soft robots hold the potential to expedite autonomous interventions, such as tissue palpation for cancer detection. While cameras are prevalent in surgical instruments, situations with obscured views necessitate palpation. This proof-of-concept study investigates the effectiveness of using a soft robot integrated with Electrical Impedance Tomography (EIT) capabilities for tissue palpation in simulated inspection of the large intestine. The approach involves classifying tissue samples of varying thickness into healthy and cancerous tissues using the shape changes induced on a hydraulically-driven soft continuum robot during palpation. Shape changes of the robot are mapped using EIT, providing arrays of impedance measurements. Following the fabrication of an in-plane bending soft manipulator, the preliminary tissue phantom design is detailed. The phantom, representing the descending colon wall, considers induced stiffness by surrounding tissues based on a mass-spring model. The shape changes of the manipulator, resulting from interactions with tissues of different stiffness, are measured, and EIT measurements are fed into a Long Short-Term Memory (LSTM) classifier. Train and test datasets are collected as temporal sequences of data from a single training phantom and two test phantoms, namely, A and B, possessing distinctive thickness patterns. The collected dataset from phantom B, which differs in stiffness distribution, remains unseen to the network, thus posing challenges to the classifier. The classifier and proposed method achieve an accuracy of and on phantom A and B, respectively. Classification results are presented through confusion matrices and heat maps, visualising the accuracy of the algorithm and corresponding classified tissues.
软机器人在医疗程序中的集成显著改善了诊断和治疗干预，解决了安全问题并提高了外科医生的灵活性。与人工智能相结合，这些软机器人有潜力加快自主干预，例如用于癌症检测的组织触诊。虽然摄像头在手术器械中很普遍，但在视野受阻的情况下仍需要进行触诊。这项概念验证研究调查了使用集成了电阻抗断层扫描（EIT）功能的软机器人在模拟大肠检查中进行组织触诊的有效性。该方法涉及在触诊过程中利用液压驱动的软连续体机器人上诱导的形状变化，将不同厚度的组织样本分类为健康组织和癌组织。利用EIT绘制机器人的形状变化，提供阻抗测量阵列。在制造平面内弯曲软机械手之后，详细介绍了初步的组织模型设计。该模型代表降结肠壁，基于质量弹簧模型考虑周围组织引起的刚度。测量机械手与不同刚度组织相互作用产生的形状变化，并将EIT测量结果输入长短期记忆（LSTM）分类器。训练和测试数据集作为来自单个训练模型和两个测试模型（即A和B）的时间序列数据收集，这两个测试模型具有独特的厚度模式。来自模型B的收集数据集在刚度分布上有所不同，网络无法看到，因此对分类器构成挑战。分类器和所提出的方法在模型A和B上的准确率分别达到了[具体准确率数值1]和[具体准确率数值2]。通过混淆矩阵和热图展示分类结果，直观呈现算法的准确性和相应的分类组织。
